Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora

Bilingual Word Embeddings from Parallel and Non-parallel Corpora for Cross-Language Text Classification

Zero-shot Cross-lingual Transfer without Parallel Corpus

Recently, although pre-trained language models have achieved great success on multilingual NLP (Natural Language Processing) tasks, the lack of training data on many tasks in low-resource languages still limits their per…

Learning bilingual word embeddings with (almost) no bilingual data

Most methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs. This has motivated an active research line to relax this requirement, with methods tha…

Bilingual Synchronization: Restoring Translational Relationships with Editing Operations

Machine Translation (MT) is usually viewed as a one-shot process that generates the target language equivalent of some source text from scratch. We consider here a more general setting which assumes an initial target seq…

Contrastive Clustering to Mine Pseudo Parallel Data for Unsupervised Translation

Modern unsupervised machine translation systems mostly train their models by generating synthetic parallel training data from large unlabeled monolingual corpora of different languages through various means, such as iter…